Resolving Newcomb's Paradox Through a Time-Field Interpretation of Decision Processes

Newcomb’s paradox is a well-known problem in decision theory that appears to create a conflict between causal reasoning and statistical prediction. The paradox arises when a highly accurate predictor forecasts a player’s future decision regarding two boxes containing different rewards. Traditional interpretations treat time as a passive ordering parameter in which past events are fixed and independent from present decisions. Under this assumption, causal decision theory recommends taking both boxes, while evidential reasoning favors taking only one box. This work proposes an alternative interpretation in which time is treated as a structured field capable of carrying correlations between events. Within this framework, the predictor’s output and the player’s decision are not independent causal events but correlated expressions of the same temporal configuration. By modeling prediction and decision as functions of a shared temporal field, the apparent paradox dissolves without invoking backward causation. The predictor simply measures patterns within the temporal structure that also govern the decision process. This interpretation clarifies the origin of the paradox and provides a conceptual bridge between decision theory, prediction systems, and models of time as an informational structure.